Applied AI ML Associate Senior

Job responsibilities

• Works with product managers, data scientists, ML engineers, and other stakeholders to understand requirements.
• Designs, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives.
• Develops and maintain automated pipelines for model/product deployment, ensuring scalability, reliability, and efficiency.
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  Conducts thorough evaluations of generative models (e.g., GPT-4), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications.
• Implements monitoring mechanisms to track model performance in real-time and ensure model reliability.
• Stays informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.

Required qualifications, capabilities, and skills

• Formal training or certification on ML Engineering or Data Science concepts and 3+ years applied experience
• Expertise in designing and implementing AI/ML pipelines. Experience in applied AI/ML engineering, with a track record of developing and deploying business critical GenAI, machine learning models in production.
• Experience in delivering agentic solutions in production. Proficiency in programming languages like Python for model development, experimentation, and integration with Azure OpenAI API.
• Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and Langchain/Llamaindex, prompting strategies, RAG

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  Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
• Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization.
• Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures.
• Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications. Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.

• Familiarity with the financial services industries.
• Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.
• A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs.